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Learning-Based Sparse Sensing With Performance Guarantees
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DOI：10.1109/TAC.2024.3424368.png)
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Abstract
En 中文
In this study, we address the challenge of sensor scheduling in discrete-time linear dynamical networks. We propose a novel learning-based rounding method aimed at converting a provided weighted sensor schedule into a sparse, unweighted schedule while preserving a comparable level of observability performance to the original weighted schedule. We introduce the notion of L-systemic performance measures, which enjoy characteristics such as homogeneity, monotonicity, convexity, and Lipschitz continuity, covering a range of well-known measures. We integrate the initialization of the weighted sensor schedule, achieved via a convex relaxation of a combinatorial optimization problem based on an L-systemic measure, into our rounding approach. We show that this produces an unweighted sensor schedule that achieves a (1 + & varepsilon;) near-optimal approximation solution while ensuring system observability. Our polynomial-time deterministic framework provides a performance guarantee compared to the optimal solution for all types of L-systemic performance measures, including a class of nonsubmodular metrics. The effectiveness of the theoretical findings is evaluated for a benchmark numerical example in distributed frequency control.
Keywords:
Observability
Vectors
Estimation
Schedules
Robot sensing systems
Linear systems
Eigenvalues and eigenfunctions
Near-optimal approximation
nonsubmodularity
observability
regret minimization
sensor networks
time-varying scheduling
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W